Xin Qiu
GitHub
parent
ff83fad400
commit
5f13700c9f
2 changed files with 380 additions and 0 deletions
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@ -1598,4 +1598,18 @@ def _optimize_post(model, lightweight_bmm=False):
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module.StableLmModel,
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module.StableLmModel,
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stablelm_model_forward
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stablelm_model_forward
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)
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)
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elif model.config.model_type == 'minicpm':
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from ipex_llm.transformers.models.minicpm import minicpm_attention_forward
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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convert_forward(model,
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module.MiniCPMMLP,
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llama_mlp_forward)
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convert_forward(model,
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module.MiniCPMRMSNorm,
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llama_rms_norm_forward)
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convert_forward(model,
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module.MiniCPMAttention,
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minicpm_attention_forward)
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return model
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return model
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366
python/llm/src/ipex_llm/transformers/models/minicpm.py
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366
python/llm/src/ipex_llm/transformers/models/minicpm.py
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@ -0,0 +1,366 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/llama/modeling_llama.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import warnings
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import importlib
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import torch.nn as nn
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from typing import Optional, Tuple, Union, List
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import math
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import os
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import torch.nn.functional as F
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import SILU
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from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
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restore_fp8_kv_cache, use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
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apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_fp8
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from ipex_llm.transformers.models.utils import mlp_fusion_check, fp16_fusion_check
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from ipex_llm.transformers.models.utils import use_decoding_fast_path
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.models.llama.modeling_llama import LlamaModel
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from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS, FP4
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from ipex_llm.ggml.quantize import ggml_tensor_qtype
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.llama import should_use_fuse_rope, should_use_xetla_mm_qkv
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from ipex_llm.transformers.models.llama import fuse_qkv_weight_xetla, repeat_kv, native_sdp
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from ipex_llm.transformers.models.llama import llama_decoding_fast_path_qtype_check
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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from transformers import logging
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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def minicpm_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[List[torch.FloatTensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[torch.FloatTensor]]]:
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forward_function = minicpm_attention_forward_original
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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kwargs=kwargs
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)
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def minicpm_attention_forward_original(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[List[torch.FloatTensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[torch.FloatTensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. "
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"Please make sure use `attention_mask` instead.`"
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)
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bsz, q_len, hidden_size = hidden_states.size()
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device = hidden_states.device
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# for flash attention
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original_dtype = hidden_states.dtype
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, seq_len=q_len)
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no_tp = not self.config.pretraining_tp > 1
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decoding_fast_path = use_decoding_fast_path(self.q_proj,
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use_fuse_rope,
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enough_kv_room,
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bsz * q_len,
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llama_decoding_fast_path_qtype_check) and no_tp
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# single batch decoding fast path
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# forward_qkv takes will perform QKV projection, rotary position embedding
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# and save the key/value states to cache, then return query states and the
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# extended key/value cache
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if decoding_fast_path:
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hidden_states = hidden_states.view(1, -1)
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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kv_seq_len = cache_k.shape[-2]
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import xe_linear
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query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
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self.q_proj.weight,
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self.k_proj.weight,
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self.v_proj.weight,
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position_ids,
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cache_k, cache_v,
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self.q_proj.weight.qtype,
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self.v_proj.weight.qtype,
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kv_seq_len,
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self.head_dim,
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self.rotary_emb.base,)
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kv_seq_len += 1
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# update past_key_value's seem_tokens and kv caches.
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if self.layer_idx == 0:
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past_key_value.seen_tokens = kv_seq_len
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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else:
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if self.config.pretraining_tp > 1:
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key_value_slicing = ((self.num_key_value_heads * self.head_dim) //
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self.config.pretraining_tp)
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query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim)
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// self.config.pretraining_tp, dim=0)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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query_states = [F.linear(hidden_states, query_slices[i])
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for i in range(self.config.pretraining_tp)]
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query_states = torch.cat(query_states, dim=-1)
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key_states = [F.linear(hidden_states, key_slices[i])
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for i in range(self.config.pretraining_tp)]
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key_states = torch.cat(key_states, dim=-1)
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value_states = [F.linear(hidden_states, value_slices[i])
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for i in range(self.config.pretraining_tp)]
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value_states = torch.cat(value_states, dim=-1)
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else:
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if fp16_fusion_check(self.q_proj, hidden_states, self.training) and \
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hidden_size == 4096 and self.q_proj.out_features == self.k_proj.out_features:
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# only use mm_qkv_out on pvc for llama-7b
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if not hasattr(self, "qkv_proj_weight"):
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self.qkv_proj_weight = torch.stack([self.q_proj.weight,
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self.k_proj.weight,
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self.v_proj.weight]).contiguous()
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self.q_proj.weight.data = self.qkv_proj_weight[0, :, :]
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self.k_proj.weight.data = self.qkv_proj_weight[1, :, :]
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self.v_proj.weight.data = self.qkv_proj_weight[2, :, :]
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torch.xpu.empty_cache()
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query_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
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dtype=hidden_states.dtype, device=hidden_states.device)
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key_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
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dtype=hidden_states.dtype, device=hidden_states.device)
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value_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
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dtype=hidden_states.dtype, device=hidden_states.device)
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torch.ops.torch_ipex.mm_qkv_out(
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hidden_states, self.qkv_proj_weight, None,
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query_states, key_states, value_states
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)
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else:
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if should_use_xetla_mm_qkv(self, device):
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if not hasattr(self, "qkv_proj_qweight"):
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self.qkv_proj_qweight = fuse_qkv_weight_xetla(self.q_proj,
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self.k_proj,
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self.v_proj,
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self.q_proj.weight.qtype,)
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import xe_linear
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q_out_len = self.q_proj.out_len
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k_out_len = self.k_proj.out_len
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v_out_len = self.v_proj.out_len
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qkv_states = xe_linear.mm_xetla(hidden_states,
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self.qkv_proj_qweight,
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self.q_proj.weight.qtype)
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query_states = qkv_states[:, :, :q_out_len]
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key_states = qkv_states[:, :, q_out_len:q_out_len + k_out_len]
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value_states = qkv_states[:, :, q_out_len + k_out_len:]
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len,
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self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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if self.layer_idx is None:
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invalidInputError(False,
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"The cache structure has changed since version v4.36. "
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f"If you are using {self.__class__.__name__} for "
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"auto-regressive decodingwith k/v caching, please make sure "
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"to initialize the attention class with a layer index.")
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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if use_fuse_rope:
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rope_theta = self.rotary_emb.base
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"llama",
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rope_theta=rope_theta)
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else:
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if cache_position is not None:
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# for transformers 4.38.0
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cos, sin = self.rotary_emb(value_states, position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "llama2")
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "llama")
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if past_key_value is not None:
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# update the number of seen tokens
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if self.layer_idx == 0:
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past_key_value.seen_tokens += key_states.shape[-2]
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# reuse k, v, self_attention
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# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
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if len(past_key_value.key_cache) <= self.layer_idx:
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past_key_value.key_cache.append(key_states)
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past_key_value.value_cache.append(value_states)
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else:
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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if not enough_kv_room:
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# allocate new
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new_c_k, new_c_v = extend_kv_cache(bsz,
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self.num_key_value_heads, # Support GQA
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self.head_dim,
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cache_k.size(2),
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kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device)
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new_c_k[:] = cache_k
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new_c_v[:] = cache_v
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cache_k = new_c_k
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cache_v = new_c_v
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key_states, value_states = append_kv_cache(cache_k,
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cache_v,
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key_states,
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value_states)
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# update past_key_value
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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if cache_position is not None:
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new_attention_mask = attention_mask[:, :, kv_seq_len - q_len:kv_seq_len, 0:kv_seq_len]
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else:
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new_attention_mask = attention_mask
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if not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, new_attention_mask):
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||||
|
# now only use flash attention for first token
|
||||||
|
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
||||||
|
key_states.to(device, dtype=torch.float16),
|
||||||
|
value_states.to(device, dtype=torch.float16),
|
||||||
|
is_causal=True)
|
||||||
|
attn_weights = None
|
||||||
|
elif not self.training and not hidden_states.requires_grad and \
|
||||||
|
self.layer_idx > 0 and \
|
||||||
|
use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
||||||
|
import xe_addons
|
||||||
|
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||||
|
new_attention_mask)
|
||||||
|
attn_output = attn_output.view(query_states.shape)
|
||||||
|
attn_weights = None
|
||||||
|
else:
|
||||||
|
# repeat k/v heads if n_kv_heads < n_heads
|
||||||
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||||
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||||
|
# otherwise, use native attention
|
||||||
|
if query_states.device.type == "xpu":
|
||||||
|
attn_output, attn_weights = native_sdp(query_states, key_states, value_states,
|
||||||
|
new_attention_mask, cache_position,
|
||||||
|
bsz, q_len, kv_seq_len,
|
||||||
|
self.head_dim, self.num_heads, output_attentions)
|
||||||
|
else:
|
||||||
|
# CPU path
|
||||||
|
if not output_attentions:
|
||||||
|
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||||
|
query_states,
|
||||||
|
key_states,
|
||||||
|
value_states,
|
||||||
|
attn_mask=new_attention_mask,
|
||||||
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
||||||
|
# The q_len > 1 is necessary to match with
|
||||||
|
# AttentionMaskConverter.to_causal_4d that
|
||||||
|
# does not create a causal mask in case q_len == 1.
|
||||||
|
is_causal=self.is_causal and new_attention_mask is None and q_len > 1,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
attn_output, attn_weights = native_sdp(query_states, key_states, value_states,
|
||||||
|
new_attention_mask, cache_position,
|
||||||
|
bsz, q_len, kv_seq_len,
|
||||||
|
self.head_dim,
|
||||||
|
self.num_heads, output_attentions)
|
||||||
|
|
||||||
|
attn_output_size = (bsz, self.num_heads, q_len, self.head_dim)
|
||||||
|
if attn_output.size() != attn_output_size:
|
||||||
|
invalidInputError(False,
|
||||||
|
f"`attn_output` should be of size {attn_output_size},"
|
||||||
|
f" but is {attn_output.size()}")
|
||||||
|
|
||||||
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||||
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||||
|
|
||||||
|
if self.config.pretraining_tp > 1:
|
||||||
|
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
||||||
|
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp,
|
||||||
|
dim=1)
|
||||||
|
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i])
|
||||||
|
for i in range(self.config.pretraining_tp)])
|
||||||
|
else:
|
||||||
|
attn_output = self.o_proj(attn_output)
|
||||||
|
|
||||||
|
if not output_attentions:
|
||||||
|
attn_weights = None
|
||||||
|
|
||||||
|
return attn_output.to(original_dtype), attn_weights, past_key_value
|
||||||
Loading…
Reference in a new issue